Can dispersal buffer against salinity‐driven zooplankton community change in Great Plains' lakes?
Bibliographic record
Abstract
Abstract The North American Great Plains contains thousands of lakes that vary in salinity from freshwater to hypersaline. Paleolimnological studies show that salinity levels in these lakes are tightly linked with climate, and current projections point to a more arid future in the region due to natural and anthropogenic climate change, potentially influencing lake salinity. Many zooplankton species are sensitive to changes in salinity, and their position near the base of the aquatic food web makes it important to understand how they might respond to increasing salinity levels. Zooplankton communities in lakes with rising salinity levels may exhibit changes in structure, including a shift toward more salinity‐tolerant species and a reduction in abundance, species richness, and diversity. However, it is possible that dispersal of zooplankton among lakes could mitigate such community changes when migrant populations replace sensitive zooplankton with those that are locally adapted to higher salinities. To test if dispersal could reduce salinity‐induced changes in zooplankton communities, we ran a field enclosure experiment at a freshwater lake in southern Saskatchewan where we manipulated salinity levels and zooplankton dispersal. We evaluated how salinity and dispersal influenced species identities and relative abundances (community structure) using multivariate statistics and comparing taxonomic and functional compositions among the different treatments (richness, diversity, and evenness). We found that increasing salinity levels in our enclosures above that in our study lake resulted in lower zooplankton abundances and species richness levels, primarily due to the loss of cladoceran species. However, patterns in our multivariate analyses suggested that cladocerans were maintained in enclosures with salinity levels of 2.5 and 5.0 g/L when those enclosures received immigration from nearby lakes. In contrast, our univariate analyses failed to find evidence that immigration affected community structure (richness, diversity, evenness). The lack of significant statistical differences could suggest that dispersal does not have an effect, or it may have been a problem with statistical power, as a power analysis suggested that fairly large effect sizes would have been required to achieve statistical significance. Based on our results, we were unable to reach a definitive conclusion on the role that dispersal might play in buffering zooplankton communities against salinity‐driven changes. However, our study provides two important insights for planning future work. First, our power analyses indicated that more replication may be needed given the variability among our experimental enclosures. Second, the patterns in our multivariate analyses suggested that cladocerans could be maintained in lakes undergoing salinity increases if they receive immigration from surrounding lakes with higher salinities. Future work examining how inter‐ and intraspecific salinity tolerance varies across lakes with a gradient of salinities would be helpful for understanding the role that dispersal might play in buffering against salinity‐driven losses of cladoceran zooplankton.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".